
"""simple docstring"""






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


UpperCAmelCase        =				logging.getLogger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	Tuple=-1			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  label_idx






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	Union[Split, str]			)       ->	List[InputExample]:





				"""simple docstring"""


				if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			):
					lowerCAmelCase												=  mode.value
				lowerCAmelCase												=  os.path.join(SCREAMING_SNAKE_CASE_      ,F"""{mode}.txt"""			)
				lowerCAmelCase												=  1
				lowerCAmelCase												=  []
				with open(SCREAMING_SNAKE_CASE_      ,encoding='''utf-8'''			) as f:
					lowerCAmelCase												=  []
					lowerCAmelCase												=  []
					for line in f:
						if line.startswith('''-DOCSTART-'''			) or line == "" or line == "\n":
							if words:
								examples.append(InputExample(guid=F"""{mode}-{guid_index}"""      ,words=SCREAMING_SNAKE_CASE_      ,labels=SCREAMING_SNAKE_CASE_			)			)
								guid_index += 1
								lowerCAmelCase												=  []
								lowerCAmelCase												=  []
						else:
							lowerCAmelCase												=  line.split(''' '''			)
							words.append(splits[0]			)
							if len(SCREAMING_SNAKE_CASE_			) > 1:
								labels.append(splits[self.label_idx].replace('''\n'''      ,''''''			)			)
							else:
								# Examples could have no label for mode = "test"
								labels.append('''O'''			)
					if words:
						examples.append(InputExample(guid=F"""{mode}-{guid_index}"""      ,words=SCREAMING_SNAKE_CASE_      ,labels=SCREAMING_SNAKE_CASE_			)			)
				return examples






			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	TextIO      ,SCREAMING_SNAKE_CASE_  :	TextIO      ,SCREAMING_SNAKE_CASE_  :	List			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  0
				for line in test_input_reader:
					if line.startswith('''-DOCSTART-'''			) or line == "" or line == "\n":
						writer.write(SCREAMING_SNAKE_CASE_			)
						if not preds_list[example_id]:
							example_id += 1
					elif preds_list[example_id]:
						lowerCAmelCase												=  line.split()[0] + ''' ''' + preds_list[example_id].pop(0			) + '''\n'''
						writer.write(SCREAMING_SNAKE_CASE_			)
					else:
						logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''      ,line.split()[0]			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	List[str]:





				"""simple docstring"""


				if path:
					with open(SCREAMING_SNAKE_CASE_      ,'''r'''			) as f:
						lowerCAmelCase												=  f.read().splitlines()
					if "O" not in labels:
						lowerCAmelCase												=  ['''O'''] + labels
					return labels
				else:
					return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class 			lowercase					( lowercase__		):






			def __init__(self  :	List[Any]			)       ->	Any:





				"""simple docstring"""


				super().__init__(label_idx=-2			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	List[str]:





				"""simple docstring"""


				if path:
					with open(SCREAMING_SNAKE_CASE_      ,'''r'''			) as f:
						lowerCAmelCase												=  f.read().splitlines()
					if "O" not in labels:
						lowerCAmelCase												=  ['''O'''] + labels
					return labels
				else:
					return [
					    "O",
					    "B-ADVP",
					    "B-INTJ",
					    "B-LST",
					    "B-PRT",
					    "B-NP",
					    "B-SBAR",
					    "B-VP",
					    "B-ADJP",
					    "B-CONJP",
					    "B-PP",
					    "I-ADVP",
					    "I-INTJ",
					    "I-LST",
					    "I-PRT",
					    "I-NP",
					    "I-SBAR",
					    "I-VP",
					    "I-ADJP",
					    "I-CONJP",
					    "I-PP",
					]





class 			lowercase					( lowercase__		):






			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Union[Split, str]			)       ->	List[InputExample]:





				"""simple docstring"""


				if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			):
					lowerCAmelCase												=  mode.value
				lowerCAmelCase												=  os.path.join(SCREAMING_SNAKE_CASE_      ,F"""{mode}.txt"""			)
				lowerCAmelCase												=  1
				lowerCAmelCase												=  []

				with open(SCREAMING_SNAKE_CASE_      ,encoding='''utf-8'''			) as f:
					for sentence in parse_incr(SCREAMING_SNAKE_CASE_			):
						lowerCAmelCase												=  []
						lowerCAmelCase												=  []
						for token in sentence:
							words.append(token['''form''']			)
							labels.append(token['''upos''']			)
						assert len(SCREAMING_SNAKE_CASE_			) == len(SCREAMING_SNAKE_CASE_			)
						if words:
							examples.append(InputExample(guid=F"""{mode}-{guid_index}"""      ,words=SCREAMING_SNAKE_CASE_      ,labels=SCREAMING_SNAKE_CASE_			)			)
							guid_index += 1
				return examples






			def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	TextIO      ,SCREAMING_SNAKE_CASE_  :	TextIO      ,SCREAMING_SNAKE_CASE_  :	List			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  0
				for sentence in parse_incr(SCREAMING_SNAKE_CASE_			):
					lowerCAmelCase												=  preds_list[example_id]
					lowerCAmelCase												=  ''''''
					for token in sentence:
						out += F"""{token["form"]} ({token["upos"]}|{s_p.pop(0			)}) """
					out += "\n"
					writer.write(SCREAMING_SNAKE_CASE_			)
					example_id += 1






			def 					UpperCAmelCase							(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	List[str]:





				"""simple docstring"""


				if path:
					with open(SCREAMING_SNAKE_CASE_      ,'''r'''			) as f:
						return f.read().splitlines()
				else:
					return [
					    "ADJ",
					    "ADP",
					    "ADV",
					    "AUX",
					    "CCONJ",
					    "DET",
					    "INTJ",
					    "NOUN",
					    "NUM",
					    "PART",
					    "PRON",
					    "PROPN",
					    "PUNCT",
					    "SCONJ",
					    "SYM",
					    "VERB",
					    "X",
					]




"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer



					class 			lowercase					( lowercase__		):






								def __init__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	int = 101			)       ->	List[str]:





									"""simple docstring"""


									lowerCAmelCase												=  length






								def __len__(self  :	Any			)       ->	List[Any]:





									"""simple docstring"""


									return self.length






								def __getitem__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





									"""simple docstring"""


									return i





					class 			lowercase					:






								def __call__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





									"""simple docstring"""


									return {"input_ids": torch.tensor(SCREAMING_SNAKE_CASE_			), "labels": torch.tensor(SCREAMING_SNAKE_CASE_			)}





					class 			lowercase					( nn.Module		):






								def __init__(self  :	List[str]			)       ->	List[Any]:





									"""simple docstring"""


									super().__init__()
									# Add some (unused) params otherwise DDP will complain.
									lowerCAmelCase												=  nn.Linear(120      ,80			)






								def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None			)       ->	Union[str, Any]:





									"""simple docstring"""


									if labels is not None:
										return torch.tensor(0.0      ,device=input_ids.device			), input_ids
									else:
										return input_ids



class 			lowercase					( lowercase__		):






			@require_torch_neuroncore
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""--output_dir {output_dir}""".split()
				lowerCAmelCase												=  ['''torchrun'''] + distributed_args + args
				execute_subprocess_async(SCREAMING_SNAKE_CASE_      ,env=self.get_env()			)
				# successful return here == success - any errors would have caused an error in the sub-call





class 			lowercase					( lowercase__		):






			@require_torch_multi_gpu
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""--output_dir {output_dir}""".split()
				lowerCAmelCase												=  ['''torchrun'''] + distributed_args + args
				execute_subprocess_async(SCREAMING_SNAKE_CASE_      ,env=self.get_env()			)
				# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					UpperCAmelCase        =				HfArgumentParser((TrainingArguments,))
					UpperCAmelCase        =				parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
					    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [1_0_1, 4_0, 7]:
										UpperCAmelCase        =				DummyDataset(dataset_length)




										def __magic_name__	(				_lowerCamelCase:		EvalPrediction			)					->						Dict:

											'''simple docstring'''




											lowerCAmelCase												=  list(range(len(_lowerCamelCase			)			)			)
											lowerCAmelCase												=  p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
											if not success and training_args.local_rank == 0:
												logger.warning(
												    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
												    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""			)
											return {"success": success}

										UpperCAmelCase        =				Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										UpperCAmelCase        =				trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										UpperCAmelCase        =				trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										UpperCAmelCase        =				2

										UpperCAmelCase        =				trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										UpperCAmelCase        =				trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)



										UpperCAmelCase        =				None


